Abstract
It was recently observed and proved by geoscientists that lightning observations from space peaked as a precursor to severe weather occurrences like flash floods, cloudbursts, tornadoes, etc. Thus, total lightning observations from space may be used to track such disasters well in advance. Satellite-based tracking is especially important in data-sparse regions (like the oceans) where the deployment of ground-based sensors is unfeasible. The Geostationary Lightning Mapper (GLM) launched in NASA’s GOES-R satellite which maps lightning by near-infrared optical transient detection is the first lightning mapper launched in a geostationary orbit. Sample time-lapse videos of these total lightning observations have been published by the GOES-R team. This work describes the challenges, optimizations and algorithms used in the application of tracking filters like the Kalman filter and particle filter for tracking lightning cells and hence storms using these videos.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lynn, B.H., Yair, Y., Price, C., Kelman, G., Clark, A.J.: Predicting cloud-to-ground and intracloud lightning in weather forecast models. Weather. Forecast. 27, 1470–1488 (2012). https://doi.org/10.1175/WAF-D-11-00144.1
Daniels, J., Goodman, S.: Towards GOES-R launch: an update on GOES-R algorithm and proving ground activities (2015)
Finke, U., Betz, H.D., Schumann, U., Laroche, P.: Lightning: Principles Instruments and Applications. Springer, Dordrecht (1999)
Niemczynowicz, J.: Storm tracking using rain gauge data. J. Hydrol. 93(1–2), 135–152 (1987). https://doi.org/10.1016/0022-1694(87)90199-5
Wolfson, M.M., Dupree, W.J., Rasmussen, R.M., Steiner, M., Benjamin, S.G., Weygandt, S.S.: Consolidated storm prediction for aviation (CoSPA), In: Integrated Communications, Navigation and Surveillance Conference, Bethesda, pp. 1–19. Bethesda, MD (2008)
Liu, C., Heckman, S.: Using total lightning data in severe storm prediction: global case study analysis from north America, Brazil and Australia. In: International Symposium on Lightning Protection, Fortaleza, pp. 20–24 (2011). https://doi.org/10.1109/SIPDA.2011.6088433
Liu, C., Heckman, S.: The application of total lightning detection and cell tracking for severe weather prediction. In: 91st American Meteorological Society Annual Meeting, pp. 1–10. Seattle (2011)
Lakshmanan, V., Smith, T.: An objective method of evaluating and devising storm-tracking algorithms. Weather. Forecast. 25, 701–709 (2010). https://doi.org/10.1175/2009WAF2222330.1
Comaniciu, D., Meer, P.: Mean shift, a robust approach toward feature space analysis. IEEE. Trans. Pattern Anal. Machine Intell. 603–619 (2002)
http://www.spc.noaa.gov/exper/archive/event.php?date=YYMMDD/. Accessed 4 Jan 2018
https://www.accuweather.com/. Accessed 7 Feb 2018
Johnson, J.T., MacKeen, P.L., Witt, A., Mitchell, E.D., Stumpf, G.J., Eilts, M.D., Thomas, K.W.: The storm cell identification and tracking algorithm: an enhanced WSR-88D algorithm. Weather. Forecast. 13, 263–276 (1998). https://doi.org/10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2
Acknowledgements
We would like to acknowledge CLASS, NOAA and GOES-R Series Program team for providing access to the GLM data and for the extended support regarding usage and documentation of satellite video imagery.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Joby, N.E., George, N.S., Geethasree, M.N., NimmiKrishna, B., Thayyil, N.R., Sankaran, P. (2020). Storm Tracking Using Geostationary Lightning Observation Videos. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_33
Download citation
DOI: https://doi.org/10.1007/978-981-32-9088-4_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9087-7
Online ISBN: 978-981-32-9088-4
eBook Packages: EngineeringEngineering (R0)